Hierarchical Predictive Control Algorithms for Optimal Design and Operation of Microgrids

TL;DR

This paper presents a high-fidelity, computationally efficient hierarchical predictive control approach for designing and operating microgrids, capable of handling complex models and long planning horizons with near-optimal solutions.

Contribution

It introduces a detailed MIQCQP model for microgrid planning and an iterative MPC algorithm that efficiently solves large-scale problems with high accuracy.

Findings

MPC algorithm scales to long planning horizons

Solutions are within 5% of optimal

High fidelity modeling improves confidence in results

Abstract

In recent years, microgrids, i.e., disconnected distribution systems, have received increasing interest from power system utilities to support the economic and resiliency posture of their systems. The economics of long distance transmission lines prevent many remote communities from connecting to bulk transmission systems and these communities rely on off-grid microgrid technology. Furthermore, communities that are connected to the bulk transmission system are investigating microgrid technologies that will support their ability to disconnect and operate independently during extreme events. In each of these cases, it is important to develop methodologies that support the capability to design and operate microgrids in the absence of transmission over long periods of time. Unfortunately, such planning problems tend to be computationally difficult to solve and those that are straightforward to solve often lack the modeling fidelity that inspires confidence in the results. To address these issues, we first develop a high fidelity model for design and operations of a microgrid that include component efficiencies, component operating limits, battery modeling, unit commitment, capacity expansion, and power flow physics; the resulting model is a mixed-integer quadratically-constrained quadratic program (MIQCQP). We then develop an iterative algorithm, referred to as the Model Predictive Control (MPC) algorithm, that allows us to solve the resulting MIQCQP. We show, through extensive computational experiments, that the MPC-based method can scale to problems that have a very long planning horizon and provide high quality solutions that lie within 5\% of optimal.